Context Graphs as an Efficient and User-Friendly Method of Describing and Recognizing a Situation in AAL

Олару, Андрей; Florea, Adina Magda

doi:10.3390/s140611110

Cited by 4 publications

(6 citation statements)

References 57 publications

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“…Third, some authors state that the use of cameras (e.g. Sarabia-Jácome et al 2020 ; Tian and Zhang 2020 ; Vourganas et al, 2020 ) and microphones (Olaru and Florea 2014 ; Tunca et al 2014 ; Navarro et al 2018 ) in particular should be excluded, as these types of sensors reveal too much information about the user. The following technologies are considered to be particularly anonymity-preserving: (a limited number of) environmental sensors, binary sensors like pressure, motion or electricity (Garcia-Ceja 2018 ), accelerometers (acceleration sensors, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Somewhat simpler is “negative monitoring”: in order not to cover all living areas of the user, algorithms can be used to infer positive values from negative ones. An example is the project by Olaru and Florea ( 2014 ), where no sensors are installed in the bathroom for privacy reasons, but the system can infer that the user is currently using the bathroom (2014, 11,127). However, a variant of anonymization can also be the subsequent anonymization of camera recordings, such as in the project by Padilla-Lopez et al ( 2015 ), in which the images are covered with blurred silhouettes in post-processing.…”

Section: Resultsmentioning

confidence: 99%

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Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

Hartmann

Primc

Rubeis

2022

Med Health Care and Philos

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AAL encompasses smart home technologies that are installed in the personal living environment in order to support older, disabled, as well as chronically ill people with the goal of delaying or reducing their need for nursing care in a care facility. Artificial intelligence (AI) is seen as an important tool for assisting the target group in their daily lives. A literature search and qualitative content analysis of 255 articles from computer science and engineering was conducted to explore the usage of ethical concepts. From an ethical point of view, the concept of independence and self-determination on the one hand and the possible loss of privacy on the other hand are widely discussed in the context of AAL. These concepts are adopted by the technical discourse in the sense that independence, self-determination and privacy are recognized as important values. Nevertheless, our research shows that these concepts have different usages and meanings in the ethical and the technical discourses. In the paper, we aim to map the different meanings of independence, self-determination and privacy as they can be found in the context of technological research on AI-based AAL systems. It investigates the interpretation of these ethical and social concepts which technicians try to build into AAL systems. In a second step, these interpretations are contextualized with concepts from the ethical discourse on AI-based assistive technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

Hartmann

Primc

Rubeis

2022

Med Health Care and Philos

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“…The performance was comparable to Hidden Markov Models and decision tree models. Olaru and Florea [26] used context graphs, context patterns, and continuous, persistent context matching to recognize context in ambient assisted living situations viewed as a set of associations between different concepts. Hao et al [27] proposed a real-time inference engine while using formal concept analysis to graphically represent the concepts that can be used to improve the recognition of sequential, interleaved, and concurrent human activity patterns.…”

Section: Related Workmentioning

confidence: 99%

Improving IoT Predictions through the Identification of Graphical Features

Akter

Holder

2019

Sensors

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IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features that are to be used by a classifier for prediction tasks. We show that a set of generic graph-based features can improve performance of sensor network predictions without the need for application-specific and task-specific feature engineering. We apply this approach to three different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. Our approach produced comparable results with most of the state-of-the-art methods, while maintaining the additional advantage of general applicability to IoT sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. We further investigate the impact of using edge-transition times, categorical features, different sensor window sizes, and normalization in the smart home domain. We also consider deep learning approaches, including the Graph Convolutional Network (GCN), for the elimination of feature engineering in the smart home domain, but our approach provided better performance in most cases. We conclude that the graphical feature-based framework that is based on IoT sensor categorization, nodes and edges as features, and feature selection techniques provides superior results when compared to the non-graph-based features.

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“…Related work in context-aware mobile computing solutions to support remote monitoring of health in general is vast and varied [ 13 ], as is that focused on context information reasoning for recognising human activity/movement [ 14 , 15 ]. Expert systems with context-aware services have also been proposed in the literature to support healthcare, including the adoption of fuzzy logic [ 16 ] and the use of sensors for recognizing user situations [ 17 ] in order to help the decision making process of health professionals and caregivers.…”

Section: Preliminaries and Related Researchmentioning

confidence: 99%

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Teles

Rocha

Silva

et al. 2017

Sensors

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Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient’s daily routine (e.g., “studying”, “at work”, “working out”). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.

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Context Graphs as an Efficient and User-Friendly Method of Describing and Recognizing a Situation in AAL

Cited by 4 publications

References 57 publications

Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

Improving IoT Predictions through the Identification of Graphical Features

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

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